{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.fftpack import fft,ifft\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# N=20取log bin的函数\n",
    "\n",
    "def databin_20(lst):\n",
    "    result = [[]]    \n",
    "    length = len(lst)\n",
    "    n = 0\n",
    "    for i in range(length):\n",
    "        result[-1].append(math.log(lst[i],10))\n",
    "        n = n+1\n",
    "        if n ==20:\n",
    "            n = 0\n",
    "            result.append([])\n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append((np.median(result[j]))) \n",
    "    return output\n",
    "\n",
    "def databin_20_std(lst):\n",
    "    result = [[]]\n",
    "    length = len(lst)\n",
    "    n = 0\n",
    "    for i in range(length):\n",
    "        result[-1].append(math.log(lst[i],10))\n",
    "        n = n+1\n",
    "        if n == 20:\n",
    "            n = 0\n",
    "            result.append([]) \n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append(np.std(result[j]))\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参考 08 Arevalo 取log bin的函数\n",
    "\n",
    "def databin_are(lst,f):\n",
    "    result = [[]]\n",
    "    f_length = len(f)\n",
    "    f_i = f[0]\n",
    "    for i in range(f_length):\n",
    "        if f[i]<=1.3*f_i:\n",
    "            result[-1].append(math.log(lst[i],10))\n",
    "        else:\n",
    "            if len(result[-1])<=2:\n",
    "                result[-1].append(math.log(lst[i],10))\n",
    "            else:\n",
    "                result.append([])\n",
    "                result[-1].append(math.log(lst[i],10))\n",
    "                f_i = f[i]\n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append(np.mean(result[j]))\n",
    "    return output        \n",
    "            \n",
    "\n",
    "def databin_are_std(lst,f):\n",
    "    result = [[]]\n",
    "    f_length = len(f)\n",
    "    f_i = f[0]\n",
    "    for i in range(f_length):\n",
    "        if f[i]<=1.3*f_i:\n",
    "            result[-1].append(math.log(lst[i],10))\n",
    "        else:\n",
    "            if len(result[-1])<=2:\n",
    "                result[-1].append(math.log(lst[i],10))\n",
    "            else:\n",
    "                result.append([])\n",
    "                result[-1].append(math.log(lst[i],10))\n",
    "                f_i = f[i]\n",
    "    result.pop()\n",
    "    output=[]\n",
    "    for j in range(len(result)):\n",
    "        output.append(np.std(result[j]))\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data18 = pd.read_csv(\"Mrk335_18_rate_03_10_tb50_sel.csv\")\n",
    "data18['RATE'] = data18['RATE'].interpolate()\n",
    "\n",
    "\n",
    "dt=50\n",
    "counts_data = data18['RATE']\n",
    "N=len(counts_data)\n",
    "pnum = np.arange(len(counts_data))\n",
    "t = [i*dt for i in pnum]\n",
    "\n",
    "\n",
    "nf = N/2\n",
    "df = 1/(dt*N)\n",
    "F_a = np.arange(1,nf+1)\n",
    "F = [i*df for i in F_a]\n",
    "F1 = F[0:int(nf)]\n",
    "mean_x = np.mean(counts_data)\n",
    "dft = fft(counts_data)\n",
    "dft1= dft[1:int(nf)+1]\n",
    "per_data = (abs(dft1)**2)*2*dt/((mean_x**2)*N)\n",
    "p_times_f_data = np.multiply(np.array(F1),np.array(per_data))\n",
    "\n",
    "F1_log_18 = [math.log(i,10) for i in F1]\n",
    "ptf_data_log_00 = [math.log(i,10) for i in p_times_f_data]\n",
    "\n",
    "# 数据分bin\n",
    "F1_binned_18 = databin_are(F1,F1)\n",
    "per_data_binned_18 = databin_are(per_data,F1)\n",
    "per_data_b_std_18 = databin_are_std(per_data,F1)\n",
    "p_times_f_data_b_18 = np.array(F1_binned_18)+np.array(per_data_binned_18)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(F1_log_18, ptf_data_log_00, color=\"b\", alpha=0.3, linewidth=1)\n",
    "plt.scatter(F1_binned_18, p_times_f_data_b_18, color=\"r\", linewidth=1) \n",
    "plt.errorbar(F1_binned_18, p_times_f_data_b_18, yerr=per_data_b_std_18, fmt='.r')\n",
    "plt.xlabel(\"log frequency\",fontsize=20)\n",
    "plt.ylabel(\"log power*frequency\",fontsize=20)\n",
    "plt.tick_params(labelsize=15)\n",
    "plt.show()\n",
    "\n",
    "perlist_18 = {'f':F1,'per':per_data}\n",
    "test = pd.DataFrame(perlist_18,columns = ['f','per'])\n",
    "test.to_csv('perlist18.csv')\n",
    "\n",
    "#perb=[10**i for i in per_data_binned_18]\n",
    "#fb=[10**i for i in F1_binned_18]\n",
    "#stdb=[10**i for i in per_data_b_std_18]\n",
    "#perlistbinned_18 = {'f':fb,'per':perb,'std':stdb}\n",
    "#test = pd.DataFrame(perlistbinned_18,columns = ['f','per','std'])\n",
    "#test.to_csv('perlistbinned18.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
